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Fix Codacy Warnings (#477)

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---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-10 15:38:45 +01:00
committed by Nicola Demo
parent e3790e049a
commit 4177bfbb50
157 changed files with 3473 additions and 3839 deletions
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121
tests/test_solver/test_supervised_solver.py
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@@ -10,22 +10,21 @@ from torch._dynamo.eval_frame import OptimizedModule
class LabelTensorProblem(AbstractProblem):
input_variables = ['u_0', 'u_1']
output_variables = ['u']
input_variables = ["u_0", "u_1"]
output_variables = ["u"]
conditions = {
'data': Condition(
input=LabelTensor(torch.randn(20, 2), ['u_0', 'u_1']),
target=LabelTensor(torch.randn(20, 1), ['u'])),
"data": Condition(
input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]),
target=LabelTensor(torch.randn(20, 1), ["u"]),
),
}
class TensorProblem(AbstractProblem):
input_variables = ['u_0', 'u_1']
output_variables = ['u']
input_variables = ["u_0", "u_1"]
output_variables = ["u"]
conditions = {
'data': Condition(
input=torch.randn(20, 2),
target=torch.randn(20, 1))
"data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1))
}
@@ -35,9 +34,7 @@ model = FeedForward(2, 1)
def test_constructor():
SupervisedSolver(problem=TensorProblem(), model=model)
SupervisedSolver(problem=LabelTensorProblem(), model=model)
assert SupervisedSolver.accepted_conditions_types == (
InputTargetCondition
)
assert SupervisedSolver.accepted_conditions_types == (InputTargetCondition)
@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
@@ -46,18 +43,20 @@ def test_constructor():
def test_solver_train(use_lt, batch_size, compile):
problem = LabelTensorProblem() if use_lt else TensorProblem()
solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=batch_size,
train_size=1.,
test_size=0.,
val_size=0.,
compile=compile)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=batch_size,
train_size=1.0,
test_size=0.0,
val_size=0.0,
compile=compile,
)
trainer.train()
if trainer.compile:
assert (isinstance(solver.model, OptimizedModule))
assert isinstance(solver.model, OptimizedModule)
@pytest.mark.parametrize("use_lt", [True, False])
@@ -65,17 +64,19 @@ def test_solver_train(use_lt, batch_size, compile):
def test_solver_validation(use_lt, compile):
problem = LabelTensorProblem() if use_lt else TensorProblem()
solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=None,
train_size=0.9,
val_size=0.1,
test_size=0.,
compile=compile)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=None,
train_size=0.9,
val_size=0.1,
test_size=0.0,
compile=compile,
)
trainer.train()
if trainer.compile:
assert (isinstance(solver.model, OptimizedModule))
assert isinstance(solver.model, OptimizedModule)
@pytest.mark.parametrize("use_lt", [True, False])
@@ -83,51 +84,59 @@ def test_solver_validation(use_lt, compile):
def test_solver_test(use_lt, compile):
problem = LabelTensorProblem() if use_lt else TensorProblem()
solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt)
trainer = Trainer(solver=solver,
max_epochs=2,
accelerator='cpu',
batch_size=None,
train_size=0.8,
val_size=0.1,
test_size=0.1,
compile=compile)
trainer = Trainer(
solver=solver,
max_epochs=2,
accelerator="cpu",
batch_size=None,
train_size=0.8,
val_size=0.1,
test_size=0.1,
compile=compile,
)
trainer.test()
if trainer.compile:
assert (isinstance(solver.model, OptimizedModule))
assert isinstance(solver.model, OptimizedModule)
def test_train_load_restore():
dir = "tests/test_solver/tmp/"
problem = LabelTensorProblem()
solver = SupervisedSolver(problem=problem, model=model)
trainer = Trainer(solver=solver,
max_epochs=5,
accelerator='cpu',
batch_size=None,
train_size=0.9,
test_size=0.1,
val_size=0.,
default_root_dir=dir)
trainer = Trainer(
solver=solver,
max_epochs=5,
accelerator="cpu",
batch_size=None,
train_size=0.9,
test_size=0.1,
val_size=0.0,
default_root_dir=dir,
)
trainer.train()
# restore
new_trainer = Trainer(solver=solver, max_epochs=5, accelerator='cpu')
new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
new_trainer.train(
ckpt_path=f'{dir}/lightning_logs/version_0/checkpoints/' +
'epoch=4-step=5.ckpt')
ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/"
+ "epoch=4-step=5.ckpt"
)
# loading
new_solver = SupervisedSolver.load_from_checkpoint(
f'{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt',
problem=problem, model=model)
f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt",
problem=problem,
model=model,
)
test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables)
assert new_solver.forward(test_pts).shape == (20, 1)
assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape
torch.testing.assert_close(
new_solver.forward(test_pts),
solver.forward(test_pts))
new_solver.forward(test_pts), solver.forward(test_pts)
)
# rm directories
import shutil
shutil.rmtree('tests/test_solver/tmp')
shutil.rmtree("tests/test_solver/tmp")